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Record W4391718243 · doi:10.1111/ffe.14254

Estimating fatigue life of carbon/epoxy composites: A rapid method coupling thermo‐mechanical analysis and residual strength

2024· article· en· W4391718243 on OpenAlexaff
Kilian Demilly, Jeanne Cavoit, Yann Marco, Gurvan Moreau, Guillaume Dolo, Nicolas Carrère

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsSafran Electronics (Canada)
FundersAgence Nationale de la RechercheNaval Group
KeywordsFatigue limitMaterials sciencePiecewise linear functionResidualResidual stressStructural engineeringCoupling (piping)EpoxyDissipationResidual strengthVibration fatigueViscoelasticityLimit (mathematics)Composite materialFatigue testingMathematicsAlgorithmMathematical analysisEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract The investigation of fatigue behavior typically involves time‐consuming tests, leading some researchers to explore methodologies based on self‐heating tests to reduce the process. For composite materials, the conventional approach involves piecewise linear approximations of the self‐heating curve and the stress transition between the first and second regimes is arbitrarily associated with a fatigue lifetime equal to 10 6 cycles. This paper proposes a novel methodology to address these simplifications. First, a non‐linear viscoelastic model is used to describe the self‐heating curve. Based on the mechanisms established in the literature, a link is proposed between the dissipation and the fatigue limit. The load leading to a significant contribution of non‐linear mechanisms is associated with an infinite life time. It allows the identification of an – curve and prediction of fatigue behavior through a minimal number of tests. The comparison with fatigue results is satisfactory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.273
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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